Artificial Intelligence

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📂 Full description In this course, AI expert Pascal Bornet presents an exploration into agentic AI—systems that don't merely suggest but take autonomous action. Based on his book Agentic Artificial Intelligence and years of implementation experience across organizations, this course cuts through the hype to deliver practical, actionable insights. Agentic AI is about building digital teammates that plan, decide, and execute multi-step tasks independently. This frees us from tedious work to focus on meaningful activities, creating faster operations, lower costs, and fewer mistakes. Discover powerful new business models, learn to drive tangible organizational impact, and gain tools to supercharge productivity in this rapidly changing landscape.

🔅 Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work, and Life 🌐 Author: Pascal Bornet 🔰 Level: Intermediate ⏰ Duration: 56m

🌀 Learn the skills and knowledge to harness the power of agentic AI responsibly.

📗 Topics: AI Agents, Artificial Intelligence for Business, Artificial Intelligence 📤 Join Artificial Intelligence and Machine Learning for more courses

🔗 Tools Every AI Engineer Should Know 1. Data Science Tools Python: Preferred language with libraries like NumPy, Pandas, Scikit-learn. R: Ideal for statistical analysis and data visualization. Jupyter Notebook: Interactive coding environment for Python and R. MATLAB: Used for mathematical modeling and algorithm development. RapidMiner: Drag-and-drop platform for machine learning workflows. KNIME: Open-source analytics platform for data integration and analysis. 2. Machine Learning Tools Scikit-learn: Comprehensive library for traditional ML algorithms. XGBoost & LightGBM: Specialized tools for gradient boosting. TensorFlow: Open-source framework for ML and DL. PyTorch: Popular DL framework with a dynamic computation graph. H2O.ai: Scalable platform for ML and AutoML. Auto-sklearn: AutoML for automating the ML pipeline. 3. Deep Learning Tools Keras: User-friendly high-level API for building neural networks. PyTorch: Excellent for research and production in DL. TensorFlow: Versatile for both research and deployment. ONNX: Open format for model interoperability. OpenCV: For image processing and computer vision. Hugging Face: Focused on natural language processing. 4. Data Engineering Tools Apache Hadoop: Framework for distributed storage and processing. Apache Spark: Fast cluster-computing framework. Kafka: Distributed streaming platform. Airflow: Workflow automation tool. Fivetran: ETL tool for data integration. dbt: Data transformation tool using SQL. 5. Data Visualization Tools Tableau: Drag-and-drop BI tool for interactive dashboards. Power BI: Microsoft’s BI platform for data analysis and visualization. Matplotlib & Seaborn: Python libraries for static and interactive plots. Plotly: Interactive plotting library with Dash for web apps. D3.js: JavaScript library for creating dynamic web visualizations. 6. Cloud Platforms AWS: Services like SageMaker for ML model building. Google Cloud Platform (GCP): Tools like BigQuery and AutoML. Microsoft Azure: Azure ML Studio for ML workflows. IBM Watson: AI platform for custom model development. 7. Version Control and Collaboration Tools Git: Version control system. GitHub/GitLab: Platforms for code sharing and collaboration. Bitbucket: Version control for teams. 8. Other Essential Tools Docker: For containerizing applications. Kubernetes: Orchestration of containerized applications. MLflow: Experiment tracking and deployment. Weights & Biases (W&B): Experiment tracking and collaboration. Pandas Profiling: Automated data profiling. BigQuery/Athena: Serverless data warehousing tools. Mastering these tools will ensure you are well-equipped to handle various challenges across the AI lifecycle.

💡 Machine Learning Projects

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📂 Full description Supercharge your software development with the features available in Cursor. This course teaches you to install and set up Cursor, how to refactor code efficiently with AI features, introduce additional context to the AI, compose new projects from scratch, and even how to generate code from images. With AI-powered features, youll write, optimize, and build applications with unprecedented speed and accuracy, making your coding workflow more effective than ever.

🔅 Accelerate Development with Artificial Intelligence and Cursor 🌐 Author: Ray Villalobos 🔰 Level: General ⏰ Duration: 29m

🌀 Discover how Cursor can transform your coding workflow with AI-assisted development using chat. Learn to compose, refactor, and build software faster and more efficiently than ever.

📗 Topics: AI Software Development, Generative AI, Integrated Development Environments 📤 Join Artificial Intelligence and Machine Learning for more courses

Generative vs. discriminative models in ML: Generative models: - learn the distribution so they can generate new samples. - possess discriminative properties, we can use them for classification. Discriminative models don't have generative properties.

🔰 Understanding Probability Distributions for Machine Learning with Python In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python. This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading. 🔗 Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/

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🌟 MatAnyone: A model for detecting people in videos using masks. MatAnyOne is a memory-based model for video matting designed to produce stable and accurate results in real-world post-production scenarios. Unlike methods that require additional annotation, MatAnyOne uses only video frames and a target object segmentation mask defined on the first frame. MatAnyOne employs region-adaptive memory fusion, where regions with small changes retain data from the previous frame, while regions with large changes rely more on information from the current frame. This technique allows MatAnyOne to efficiently track a target object, even in complex and ambiguous scenes, while preserving sharp edges and intact foreground parts. The model was created using a unique training strategy that relies on segmentation data to improve the stability of object extraction. Unlike common practices, MatAnyOne uses this data directly in the same branch as the mask data. This is achieved by applying region-specific losses: a pixel-wise loss for core regions and an improved DDC loss for boundary regions. For training, a custom dataset VM800 was specially created, which is twice as large, more diverse and better quality than VideoMatte240K, which ultimately significantly improved the reliability of training object selection on video. In tests, MatAnyOne showed high results compared to existing methods on both synthetic and real videos: 🟠 On VideoMatte and YouTubeMatte, MatAnyOne has the best results in MAD (mean absolute difference) and dtSSD (shape transform distance); 🟢 In the real-world video benchmark, MatAnyOne achieved MAD 0.18, MSE 0.11, and dtSSD 0.95, which is significantly better than RVM10 (MAD 1.21, MSE 0.77, dtSSD 1.43) and MaGGIe12 (MAD 1.94, MSE 1.53, dtSSD 1.63). ⚠️ According to the discussion in the repository issues , MatAnyOne is capable of working locally from 4 GB VRAM and higher with short-duration videos. The developer has not published any real technical criteria. ▶️ Local installation and launch of web-demo on Gradio:

 # Clone Repo
git clone https://github.com/pq-yang/MatAnyone
cd MatAnyone

# Create Conda env and install dependencies
conda create -n matanyone python=3.8 -y
conda activate matanyone

pip install -e .

# Install python dependencies for gradio
pip3 install -r hugging_face/requirements.txt

# Launch the demo
python app.py

🟡 Project page 🟡 Model 🟡 Arxiv 🟡 Demo 🖥 GitHub

🧠 Different types of Machine Learning